import numpy as np
import cv2
import matplotlib.pyplot as plt

# cap = cv2.VideoCapture('../IMAGES/video_mask.avi')
path = '../../../../../large_data/video/cam/save_blue_video.avi'
cap = cv2.VideoCapture(path)

lower_blue = np.array([110, 50, 50])
upper_blue = np.array([130, 255, 255])

# take first frame of the video
ret,frame = cap.read()
print(frame.shape)
plt.imshow(frame[:,:,::-1])
plt.show()

# setup initial location of window
r,h,c,w = 190,60,550,60  # simply hardcoded the values
track_window = (c,r,w,h)
# set up the ROI for tracking
roi = frame[r:r+h, c:c+w]
plt.imshow(roi[:,:,::-1])
plt.show()

hsv_roi =  cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, lower_blue, upper_blue)

roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])
cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
while(1):
    ret ,frame = cap.read()
    if ret == True:
        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
        dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
        # apply meanshift to get the new location
        ret, track_window = cv2.meanShift(dst, track_window, term_crit)
        # Draw it on image
        x,y,w,h = track_window
        img2 = cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0),2)
        cv2.imshow('img2',img2)
        k = cv2.waitKey(100) & 0xff
        if k == 27:
            break
        else:
            cv2.imwrite("1.jpg",img2)
    else:
        break
cv2.destroyAllWindows()
cap.release()
